Influence of Interferon-γ on the Extent and Phenotype of Diet-Induced Atherosclerosis in the LDLR-Deficient Mouse
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate the influence of interferon-gamma (IFN-gamma) on atherosclerosis in low density lipoprotein receptor (LDLR)-null mice. METHODS AND RESULTS: We cross-bred IFN-gamma-deficient mice with LDLR-null mice and analyzed lipoprotein profiles and atherosclerosis in the compound mutant progeny after 8 and 20 weeks on a cholesterol-enriched diet. IFN-gamma deficiency did not affect serum cholesterol levels or lipoprotein profiles, but it did affect the extent and phenotype of atherosclerosis. Atherosclerotic lesions in IFN-gamma-deficient mice were reduced by 75% in the aortic arch and by 46% in the descending aorta compared with control mice after 8 weeks on the diet. After 20 weeks, arch lesions were reduced by 43%, and descending aorta lesions were reduced by 65% in IFN-gamma-deficient mice compared with controls. At 8 weeks, percent lesional macrophage and smooth muscle content was significantly less in the IFN-gamma-deficient mice, but not at 20 weeks. Although there were fewer class II major histocompatibility complex-positive cells in the lesions of IFN-gamma-deficient animals compared with controls, class II major histocompatibility complex expression on endothelial cells overlying lesions persisted in the absence of IFN-gamma. CONCLUSIONS: These data provide direct evidence that IFN-gamma influences atherosclerosis development and phenotype in the LDLR-deficient mouse, independent of changes in blood lipoprotein profiles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".